{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/OpensearchDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opensearch Vector Store\n",
    "\n",
    "Elasticsearch only supports Lucene indices, so only Opensearch is supported."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note on setup**: We setup a local Opensearch instance through the following doc. https://opensearch.org/docs/1.0/\n",
    "\n",
    "If you run into SSL issues, try the following `docker run` command instead: \n",
    "```\n",
    "docker run -p 9200:9200 -p 9600:9600 -e \"discovery.type=single-node\" -e \"plugins.security.disabled=true\" opensearchproject/opensearch:1.0.1\n",
    "```\n",
    "\n",
    "Reference: https://github.com/opensearch-project/OpenSearch/issues/1598"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-readers-elasticsearch\n",
    "%pip install llama-index-vector-stores-opensearch\n",
    "%pip install llama-index-embeddings-ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p 'data/paul_graham/'\n",
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from os import getenv\n",
    "from llama_index.core import SimpleDirectoryReader\n",
    "from llama_index.vector_stores.opensearch import (\n",
    "    OpensearchVectorStore,\n",
    "    OpensearchVectorClient,\n",
    ")\n",
    "from llama_index.core import VectorStoreIndex, StorageContext\n",
    "\n",
    "# http endpoint for your cluster (opensearch required for vector index usage)\n",
    "endpoint = getenv(\"OPENSEARCH_ENDPOINT\", \"http://localhost:9200\")\n",
    "# index to demonstrate the VectorStore impl\n",
    "idx = getenv(\"OPENSEARCH_INDEX\", \"gpt-index-demo\")\n",
    "# load some sample data\n",
    "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpensearchVectorClient stores text in this field by default\n",
    "text_field = \"content\"\n",
    "# OpensearchVectorClient stores embeddings in this field by default\n",
    "embedding_field = \"embedding\"\n",
    "# OpensearchVectorClient encapsulates logic for a\n",
    "# single opensearch index with vector search enabled\n",
    "client = OpensearchVectorClient(\n",
    "    endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field\n",
    ")\n",
    "# initialize vector store\n",
    "vector_store = OpensearchVectorStore(client)\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "# initialize an index using our sample data and the client we just created\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    documents=documents, storage_context=storage_context\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> [query] Total LLM token usage: 29628 tokens\n",
      "INFO:root:> [query] Total embedding token usage: 8 tokens\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n\\nThe author grew up writing short stories, programming on an IBM 1401, and building a computer kit from Heathkit. They also wrote programs for a TRS-80, such as games, a program to predict model rocket flight, and a word processor. After years of nagging, they convinced their father to buy a TRS-80, and they wrote simple games, a program to predict how high their model rockets would fly, and a word processor that their father used to write at least one book. In college, they studied philosophy and AI, and wrote a book about Lisp hacking. They also took art classes and applied to art schools, and experimented with computer graphics and animation, exploring the use of algorithms to create art. Additionally, they experimented with machine learning algorithms, such as using neural networks to generate art, and exploring the use of numerical values to create art. They also took classes in fundamental subjects like drawing, color, and design, and applied to two art schools, RISD in the US, and the Accademia di Belli Arti in Florence. They were accepted to RISD, and while waiting to hear back from the Accademia, they learned Italian and took the entrance exam in Florence. They eventually graduated from RISD'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run query\n",
    "query_engine = index.as_query_engine()\n",
    "res = query_engine.query(\"What did the author do growing up?\")\n",
    "res.response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The OpenSearch vector store supports [filter-context queries](https://opensearch.org/docs/latest/query-dsl/query-filter-context/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import Document\n",
    "from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter\n",
    "import regex as re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the text into paragraphs.\n",
    "text_chunks = documents[0].text.split(\"\\n\\n\")\n",
    "\n",
    "# Create a document for each footnote\n",
    "footnotes = [\n",
    "    Document(\n",
    "        text=chunk,\n",
    "        id=documents[0].doc_id,\n",
    "        metadata={\"is_footnote\": bool(re.search(r\"^\\s*\\[\\d+\\]\\s*\", chunk))},\n",
    "    )\n",
    "    for chunk in text_chunks\n",
    "    if bool(re.search(r\"^\\s*\\[\\d+\\]\\s*\", chunk))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert the footnotes into the index\n",
    "for f in footnotes:\n",
    "    index.insert(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"The author believes that any sufficiently advanced alien civilization would know about the Pythagorean theorem and possibly also about Lisp in McCarthy's 1960 paper.\""
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a query engine that only searches certain footnotes.\n",
    "footnote_query_engine = index.as_query_engine(\n",
    "    filters=MetadataFilters(\n",
    "        filters=[\n",
    "            ExactMatchFilter(\n",
    "                key=\"term\", value='{\"metadata.is_footnote\": \"true\"}'\n",
    "            ),\n",
    "            ExactMatchFilter(\n",
    "                key=\"query_string\",\n",
    "                value='{\"query\": \"content: space AND content: lisp\"}',\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "\n",
    "res = footnote_query_engine.query(\n",
    "    \"What did the author about space aliens and lisp?\"\n",
    ")\n",
    "res.response"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use reader to check out what VectorStoreIndex just created in our index.\n",
    "\n",
    "Reader works with Elasticsearch too as it just uses the basic search features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "embedding dimension: 1536\n",
      "all fields in index: dict_keys(['content', 'embedding'])\n"
     ]
    }
   ],
   "source": [
    "# create a reader to check out the index used in previous section.\n",
    "from llama_index.readers.elasticsearch import ElasticsearchReader\n",
    "\n",
    "rdr = ElasticsearchReader(endpoint, idx)\n",
    "# set embedding_field optionally to read embedding data from the elasticsearch index\n",
    "docs = rdr.load_data(text_field, embedding_field=embedding_field)\n",
    "# docs have embeddings in them\n",
    "print(\"embedding dimension:\", len(docs[0].embedding))\n",
    "# full document is stored in metadata\n",
    "print(\"all fields in index:\", docs[0].metadata.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of chunks: 10\n"
     ]
    }
   ],
   "source": [
    "# we can check out how the text was chunked by the `GPTOpensearchIndex`\n",
    "print(\"total number of chunks created:\", len(docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chunks that mention Lisp: 10\n",
      "chunks that mention Yahoo: 8\n"
     ]
    }
   ],
   "source": [
    "# search index using standard elasticsearch query DSL\n",
    "docs = rdr.load_data(text_field, {\"query\": {\"match\": {text_field: \"Lisp\"}}})\n",
    "print(\"chunks that mention Lisp:\", len(docs))\n",
    "docs = rdr.load_data(text_field, {\"query\": {\"match\": {text_field: \"Yahoo\"}}})\n",
    "print(\"chunks that mention Yahoo:\", len(docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hybrid query for opensearch vector store\n",
    "Hybrid query has been supported since OpenSearch 2.10. It is a combination of vector search and text search. It is useful when you want to search for a specific text and also want to filter the results by vector similarity. You can find more details: https://opensearch.org/docs/latest/query-dsl/compound/hybrid/. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Search Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new [search pipeline](https://opensearch.org/docs/latest/search-plugins/search-pipelines/creating-search-pipeline/) with [score normalization and weighted harmonic mean combination](https://opensearch.org/docs/latest/search-plugins/search-pipelines/normalization-processor/).\n",
    "\n",
    "```\n",
    "PUT /_search/pipeline/hybrid-search-pipeline\n",
    "{\n",
    "  \"description\": \"Post processor for hybrid search\",\n",
    "  \"phase_results_processors\": [\n",
    "    {\n",
    "      \"normalization-processor\": {\n",
    "        \"normalization\": {\n",
    "          \"technique\": \"min_max\"\n",
    "        },\n",
    "        \"combination\": {\n",
    "          \"technique\": \"harmonic_mean\",\n",
    "          \"parameters\": {\n",
    "            \"weights\": [\n",
    "              0.3,\n",
    "              0.7\n",
    "            ]\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize a OpenSearch client and vector store supporting hybrid query with search pipeline details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import getenv\n",
    "from llama_index.vector_stores.opensearch import (\n",
    "    OpensearchVectorStore,\n",
    "    OpensearchVectorClient,\n",
    ")\n",
    "\n",
    "# http endpoint for your cluster (opensearch required for vector index usage)\n",
    "endpoint = getenv(\"OPENSEARCH_ENDPOINT\", \"http://localhost:9200\")\n",
    "# index to demonstrate the VectorStore impl\n",
    "idx = getenv(\"OPENSEARCH_INDEX\", \"auto_retriever_movies\")\n",
    "\n",
    "# OpensearchVectorClient stores text in this field by default\n",
    "text_field = \"content\"\n",
    "# OpensearchVectorClient stores embeddings in this field by default\n",
    "embedding_field = \"embedding\"\n",
    "# OpensearchVectorClient encapsulates logic for a\n",
    "# single opensearch index with vector search enabled with hybrid search pipeline\n",
    "client = OpensearchVectorClient(\n",
    "    endpoint,\n",
    "    idx,\n",
    "    4096,\n",
    "    embedding_field=embedding_field,\n",
    "    text_field=text_field,\n",
    "    search_pipeline=\"hybrid-search-pipeline\",\n",
    ")\n",
    "\n",
    "from llama_index.embeddings.ollama import OllamaEmbedding\n",
    "\n",
    "embed_model = OllamaEmbedding(model_name=\"llama2\")\n",
    "\n",
    "# initialize vector store\n",
    "vector_store = OpensearchVectorStore(client)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLM is explicitly disabled. Using MockLLM.\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core.schema import TextNode\n",
    "from llama_index.core import VectorStoreIndex, StorageContext\n",
    "\n",
    "\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "\n",
    "nodes = [\n",
    "    TextNode(\n",
    "        text=\"The Shawshank Redemption\",\n",
    "        metadata={\n",
    "            \"author\": \"Stephen King\",\n",
    "            \"theme\": \"Friendship\",\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"The Godfather\",\n",
    "        metadata={\n",
    "            \"director\": \"Francis Ford Coppola\",\n",
    "            \"theme\": \"Mafia\",\n",
    "        },\n",
    "    ),\n",
    "    TextNode(\n",
    "        text=\"Inception\",\n",
    "        metadata={\n",
    "            \"director\": \"Christopher Nolan\",\n",
    "        },\n",
    "    ),\n",
    "]\n",
    "\n",
    "index = VectorStoreIndex(\n",
    "    nodes, storage_context=storage_context, embed_model=embed_model\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search the index with hybrid query by specifying the vector store query mode: VectorStoreQueryMode.HYBRID with filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query_strWhat is inception about?\n",
      "query_modehybrid\n",
      "{'size': 2, 'query': {'hybrid': {'queries': [{'bool': {'must': {'match': {'content': {'query': 'What is inception about?'}}}, 'filter': [{'term': {'metadata.theme.keyword': 'Mafia'}}]}}, {'script_score': {'query': {'bool': {'filter': [{'term': {'metadata.theme.keyword': 'Mafia'}}]}}, 'script': {'source': \"1/(1.0 + l2Squared(params.query_value, doc['embedding']))\", 'params': {'field': 'embedding', 'query_value': [0.41321834921836853, 0.18020285665988922, 2.5630273818969727, 1.490068793296814, -2.2188172340393066, 0.3613924980163574, 0.036182258278131485, 1.3815258741378784, -0.4603463411331177, 0.9783738851547241, 0.3667166233062744, -0.30677080154418945, -1.2893489599227905, -1.19036865234375, -1.4050743579864502, -2.200796365737915, 0.05992934852838516, 0.30156904458999634, 0.6115846633911133, -0.028691552579402924, 0.5112416744232178, -2.069373846054077, 0.6121743321418762, -0.05102552846074104, 1.8506423234939575, -1.293755292892456, -0.8149858117103577, 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3.9159140586853027, 0.17979349195957184, 1.6432956457138062, -0.40627729892730713]}}}}]}}}\n",
      "[NodeWithScore(node=TextNode(id_='657e40fb-497c-4c1a-8524-6351adbe990f', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='81cf4b9e847ba42e83fc401e31af8e17d629f0d5cf9c0c320ec7ac69dd0257e1', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.5), NodeWithScore(node=TextNode(id_='fc548a8e-5a1e-4392-bdce-08f8cb888c3f', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='81cf4b9e847ba42e83fc401e31af8e17d629f0d5cf9c0c320ec7ac69dd0257e1', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.0005)]\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n",
    "from llama_index.core.vector_stores.types import VectorStoreQueryMode\n",
    "\n",
    "filters = MetadataFilters(\n",
    "    filters=[\n",
    "        ExactMatchFilter(\n",
    "            key=\"term\", value='{\"metadata.theme.keyword\": \"Mafia\"}'\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "retriever = index.as_retriever(\n",
    "    filters=filters, vector_store_query_mode=VectorStoreQueryMode.HYBRID\n",
    ")\n",
    "\n",
    "result = retriever.retrieve(\"What is inception about?\")\n",
    "\n",
    "print(result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}
